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Martin Schumacher, Jürgen Schulte-Mönting, Peter Stoeter, Monika Warmuth-Metz and Laszlo Solymosi

Object

Data analysis was performed in a multicenter study to evaluate magnetic resonance (MR) imaging for classification of brain tumors, prognosis, and prediction of tumor histological diagnosis.

Methods

The clinical, MR imaging, and histological findings obtained in 142 pediatric cases of brainstem disease were assessed in a multicenter study performed as a blinded review. Clinical data were available in 142 cases, histopathological findings in 126, and MR images in 131. The subgroup of cases involving brainstem gliomas (78 cases) was analyzed separately. Images that met criteria for evaluation were reviewed in random order by three experienced observers who were initially blinded to clinical data as well as histopathological diagnoses. Subsequently, the images were randomized again and provided to the observers for review together with the clinical symptoms of the specific patients.

The three observers were able to correctly identify lesions as tumors or nontumorous disease on MR images in 99, 96, and 95% of cases, resulting in an overall sensitivity of 0.94, a specificity of 0.43, a positive predictive value of 0.96, and a negative predictive value of 0.45. Awareness of clinical symptoms did not change the results.

Conclusions

Based on 14 imaging criteria together with the patient’s clinical history and symptoms, laboratory data (results of cerebrospinal fluid analysis as well as infectious and immunological parameters), and imaging follow up, a diagnosis of brainstem tumor, as opposed to demyelination, encephalitis, or granuloma, could generally be made. Given these findings, there is only rarely a need for biopsy, and in those patients in whom it is considered, the potential costs and benefits must be carefully assessed on a case-by-case basis.

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Herwin Speckter, Jose Bido, Giancarlo Hernandez, Diones Rivera Mejía, Luis Suazo, Santiago Valenzuela, Eddy Perez-Then and Peter Stoeter

OBJECTIVE

Diffusion tensor imaging (DTI) parameters are able to differentiate between meningioma subtypes. The hypothesis that there is a correlation between DTI parameters and the change in tumor size after Gamma Knife radiosurgery (GKRS) was analyzed.

METHODS

DTI parameters were measured using MRI before GKRS in 26 patients with meningiomas. The findings were correlated with the change in tumor size after treatment as measured at the last follow-up (range 12.5–45 months).

RESULTS

Only those meningiomas that showed the highest fractional anisotropy (FA), the lowest spherical index of the tensor ellipsoid (Cs), and the lowest radial diffusivity (RD) either increased or remained stable in terms of volume, whereas all other meningiomas decreased in volume. The correlation between the DTI parameters (correlation values of −0.81 for FA, 0.75 for Cs, 0.66 for RD, and 0.66 for mean diffusivity) and the rate of volume change per month was significant (p ≤ 0.001). Other factors, including original tumor size, prescription dose, and patient age, did not correlate significantly.

CONCLUSIONS

Meningiomas that show high FA values—as well as low Cs, low RD, and low mean diffusivity values—do not respond as well to GKRS in comparison with meningiomas with low FA values. This finding might be due to their higher content level of fibrous tissue. In particular, the meningioma with the highest FA value (0.444) considerably increased in volume (by 32.3% after 37 months), whereas the meningioma with the lowest FA value (0.151) showed the highest rate of reduction (3.3% per month) in this study.

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Axel Thomas Stadie, Ralf Alfons Kockro, Robert Reisch, Andrei Tropine, Stephan Boor, Peter Stoeter and Axel Perneczky

Object

The authors report on their experience with a 3D virtual reality system for planning minimally invasive neurosurgical procedures.

Methods

Between October 2002 and April 2006, the authors used the Dextroscope (Volume Interactions, Ltd.) to plan neurosurgical procedures in 106 patients, including 100 with intracranial and 6 with spinal lesions. The planning was performed 1 to 3 days preoperatively, and in 12 cases, 3D prints of the planning procedure were taken into the operating room. A questionnaire was completed by the neurosurgeon after the planning procedure.

Results

After a short period of acclimatization, the system proved easy to operate and is currently used routinely for preoperative planning of difficult cases at the authors' institution. It was felt that working with a virtual reality multimodal model of the patient significantly improved surgical planning. The pathoanatomy in individual patients could easily be understood in great detail, enabling the authors to determine the surgical trajectory precisely and in the most minimally invasive way.

Conclusions

The authors found the preoperative 3D model to be in high concordance with intraoperative conditions; the resulting intraoperative “déjà-vu” feeling enhanced surgical confidence. In all procedures planned with the Dextroscope, the chosen surgical strategy proved to be the correct choice.

Three-dimensional virtual reality models of a patient allow quick and easy understanding of complex intracranial lesions.

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Herwin Speckter, Jose Bido, Giancarlo Hernandez, Diones Rivera, Luis Suazo, Santiago Valenzuela, Isidro Miches, Jairo Oviedo, Cesar Gonzalez and Peter Stoeter

OBJECTIVE

The goal of this study was to identify parameters from routine T1- and T2-weighted MR sequences and diffusion tensor imaging (DTI) that best predict the volumetric changes in a meningioma after treatment with Gamma Knife radiosurgery (GKRS).

METHODS

In 32 patients with meningioma, routine MRI and DTI data were measured before GKRS. A total of 78 parameters derived from first-level texture analysis of the pretreatment MR images, including calculation of the mean, SD, 2.5th and 97.5th percentiles, and kurtosis and skewness of data in histograms on a voxel-wise basis, were correlated with lesion volume change after a mean follow-up period of 3 years (range 19.5–63.3 months).

RESULTS

Several DTI-derived parameters correlated significantly with a meningioma volume change. The parameter that best predicted the results of GKRS was the 2.5th percentile value of the smallest eigenvalue (L3) of the diffusion tensor (correlation coefficient 0.739, p ≤ 0.001), whereas among the non-DTI parameters, only the SD of T2-weighted images correlated significantly with a tumor volume change (correlation coefficient 0.505, p ≤ 0.05, after correction for family-wise errors using false-detection-rate correction).

CONCLUSIONS

DTI-derived data had a higher correlation to shrinkage of meningioma volume after GKRS than data from T1- and T2-weighted image sequences. However, if only routine MR images are available, the SD of T2-weighted images can be used to predict control or possible progression of a meningioma after GKRS.